A recommendation system may typically be a tool used by websites to facilitate its users in locating targeted artifacts, such as, articles, music, movies or products. A sub-category of the recommendation system may include video recommendation systems. Typically, a video recommendation system may be a tool used by websites to facilitate users in locating targeted online videos, or online videos that may be of interest. Typically, video recommendation systems predict users' ratings for unseen online videos and then recommend the unseen online videos with the highest predicted users' ratings.
In the past few years, consumption of the online videos has substantially increased leading to popularity of the video recommendation systems. One of the applications of video recommendation systems may be online TV. Online TV may be defined as a supplement to traditional broadcast/cable TV. Particularly, online TV may enable users to watch online a show, a soap, a serial or a game broadcasted by the TV networks. Online TV may also enable users to watch ‘online-only content,’ such as, “Deleted Scenes” or “Highlights.”
Furthermore, while increased consumption of the online videos leads to popularity of video recommendation systems, it also results in increased number of challenges due to features and properties of the online videos. The features or properties of the online videos are weak indicators of users' interests and preferences and thus, require specialized processes and techniques for recommending the online videos. The features or properties of the videos, for example, may include significant size of video data compared to size of text or image data, low-level visual features, lack of textual matter, and the like.
Moreover, common video recommendation systems are either not employed to the online TV or only do simple online video recommendations based on meta-data or usage statistics. Furthermore, recommendation systems or video recommendation systems typically do not consider factors that significantly impact online video recommendations of TV shows. For example, the factors may include high-quality studio production of videos, short time horizon of the shows or soaps, temporal effects of the shows, and the like. The temporal effects, for example, may be an impact on interests of users due to releasing date, releasing time and time period elapsed from the release date of videos, shows or soaps. For example, a show A released recently may be of more interest to users than a show B that was released one year ago.
Hence, it is highly desirable to develop methods and systems for recommending online TV shows, soaps, games, and the like. Further, it is desirable to develop methods and systems that may address needs of the online TV networks.